fig1_theme <- theme(text=element_text(size=8, face="plain"),
strip.text=element_text(size=8, face="plain"),
plot.title=element_text(size=8, face="plain"),
axis.text=element_text(size=8),
axis.title=element_text(size=8, face="plain"),
plot.background = element_blank(),
panel.background = element_blank(),
legend.text=element_text(size=8),
legend.key.size = unit(0.5,"line"),
axis.line=element_line(size=0.12),
axis.ticks=element_line(size=0.12),
legend.margin=margin(t=0, unit='cm'),
plot.margin = margin(0.25, 0.25, 0.25, 0.25, "cm"))
fig1a <- readRDS('figures/cell_line_features/Pediatric (CRISPR only)_20q1.rds')
fig1b <- readRDS('figures/tumor_cl/tumor_cl_reannotated_pediatric_2_20q1.rds')
fig1c <- readRDS('figures/mutation/mutsig_rates_in_WES_20q1.rds')
fig1d <- readRDS('figures/mutation/peds_v_other_mutsig_across_lineages_in_WES_20q1.rds')
# decrease size of dots
fig1c$layers[[1]]$aes_params$size <- 1
fig1c$labels$y <- gsub("MutSig2CV ", "MutSig2CV\n", fig1c$labels$y)
fig1d$labels$y <- gsub("MutSig2CV ", "MutSig2CV\n", fig1d$labels$y)
# decrease size of p-value text
fig1d$layers[[3]]$aes_params$textsize <- 2
# decrease size of violin outline
fig1d$layers[[1]]$aes_params$size <- 0
# outlier alpha
fig1d$layers[[2]]$geom_params$outlier.alpha <- 0.25
# decrease size of dots
#fig1b$layers[[1]]$aes_params$size <- 2
# adjust legend
fig1a$guides$fill$nrow <- NULL
fig1a$guides$fill$ncol <- NULL
fig1b$guides$shape$override.aes$size <- 1
fig1b$guides$fill$override.aes$size <- 1
fig1b$labels$title <- "Tumor-cell line expression"
fig1b$guides$fill$title <- "Tumor type"
fig1 <- ggarrange(ggarrange(fig1a + fig1_theme +
theme(legend.position = "right",
axis.text.x=element_text(size=3),
axis.text.y=element_text(size=5),
axis.line=element_blank(),
axis.ticks = element_blank()),
geom_blank(),
widths = c(2, 0.25),
ncol = 2,
nrow = 1,
labels = c("a", "")),
ggarrange(fig1b + fig1_theme + theme(aspect.ratio=1),
geom_blank(),
widths = c(2, 0.75),
ncol = 2,
nrow = 1,
labels = c("b", "")),
ggarrange(fig1c + fig1_theme,
fig1d + fig1_theme,
widths = c(2, 0.75),
ncol = 2,
nrow = 1,
align = "h",
labels = c("c", "d")),
nrow =3,
ncol=1,
heights = c(3.5, 3, 3))
## Warning in as_grob.default(plot): Cannot convert object of class
## LayerInstanceLayerggprotogg into a grob.
## Warning in as_grob.default(plot): Cannot convert object of class
## LayerInstanceLayerggprotogg into a grob.
ggsave(fig1, filename = "final_figures/Fig1.pdf", width=20 , height=26, units='cm', useDingbats = FALSE)
print(fig1)
fig2a <- readRDS('figures/dependency/dep_example_distribution_20q1.rds')
fig2b <- readRDS('figures/mutation/depsLRT_v_mutsig_across_lineages_in_WES_20q1.rds')
fig2c <- readRDS('figures/dependency/total_lrt_dependencies_median_arranged_20q1.rds')
fig2d <- readRDS('figures/dependency/peds_v_other_LRTdeps_all_20q1.rds')
fig2e <- readRDS('figures/biomarkers/lrt_rate_vs_biomarker_20q1.rds')
fig2f <- readRDS('figures/dependency/umap_cell_line_dependency_lrt_20q1.rds')
# decrease size of lines
fig2a$labels$y <- "Distribution of cell lines"
fig2a$layers[[2]]$aes_params$size <- 4
# decrease size of dots
fig2b$layers[[1]]$aes_params$size <- 1
fig2b$labels$y <- gsub("MutSig2CV ", "MutSig2CV\n", fig2b$labels$y)
# decrease size of dots
fig2c$layers[[1]]$aes_params$size <- 1
# decrease size of p-value text
fig2d$layers[[3]]$aes_params$textsize <- 2
fig2d$layers[[3]]$aes_params$size <- 2
# decrease size of violin outline
fig2d$layers[[1]]$aes_params$size <- 0
fig2d$layers[[2]]$geom_params$outlier.alpha <- 0.25
fig2d$labels$y <- fig2c$labels$y
fig2e$labels$title <- "Predictive models"
# decrease size of dots
fig2f$layers[[1]]$aes_params$size <- 2
fig2f$labels$title <- "Dependency embedding"
fig2 <- ggarrange(ggarrange(fig2a + fig1_theme,
fig2b + fig1_theme + theme(aspect.ratio=1),
widths = c(2, 3),
ncol = 2,
nrow = 1,
labels = c("a", "b")),
ggarrange(fig2c + fig1_theme,
fig2d + fig1_theme,
widths = c(2, 0.75),
ncol = 2,
nrow = 1,
align = "h",
labels = c("c", "d")),
ggarrange(fig2e + fig1_theme + theme(aspect.ratio=1, legend.position = "bottom"),
ggarrange(fig2f + fig1_theme + theme(aspect.ratio=1, legend.position = "right"), geom_blank(),
heights = c(2, 0.3),
ncol = 1,
nrow = 2),
widths = c(2, 3.5),
ncol = 2,
nrow = 1,
align = "h",
labels = c("e", "f")),
nrow =3,
ncol=1,
heights = c(3, 3, 3.5),
align = "v")
## Warning in as_grob.default(plot): Cannot convert object of class
## LayerInstanceLayerggprotogg into a grob.
## Warning: Graphs cannot be horizontally aligned unless the axis parameter is set.
## Placing graphs unaligned.
ggsave(fig2, filename = "final_figures/Fig2.pdf", width=20 , height=26, units='cm', useDingbats = FALSE)
print(fig2)
fig3a <- readRDS('figures/targets/dep_heatmap_horz_ALK_20q1.rds')
fig3b <- readRDS('figures/targets/dep_heatmap_horz_BRAF_20q1.rds')
fig3c <- readRDS('figures/targets/dep_heatmap_horz_MDM2_MDM4_20q1.rds')
fig3d <- readRDS('figures/targets/dep_heatmap_horz_CDK4_CDK6_20q1.rds')
fig3e <- readRDS('figures/targets/dep_heatmap_horz_BCL2_MCL1_20q1.rds')
fig3_legend1 <- readRDS('figures/targets/dep_heatmap_horz_legend1_20q1.rds')
fig3_legend2 <- readRDS('figures/targets/dep_heatmap_horz_legend2_20q1.rds')
font_for_grob <- 3
names(font_for_grob) <- "italics"
for (i in 1:length(fig3_legend2$gtable[["grobs"]][[3]]$children))
{
if (!is.null(fig3_legend2$gtable[["grobs"]][[3]]$children[[i]]$label))
{
cat(i)
fig3_legend2$gtable[["grobs"]][[3]]$children[[i]]$label <- gsub("biomarker", "Biomarker", fig3_legend2$gtable[["grobs"]][[3]]$children[[i]]$label)
fig3_legend2$gtable[["grobs"]][[3]]$children[[i]]$label <- gsub("tumor_type", "Tumor type", fig3_legend2$gtable[["grobs"]][[3]]$children[[i]]$label)
}
if (!is.null(fig3_legend2$gtable[["grobs"]][[3]]$children[[i]]$gp$fontface))
{
if (fig3_legend2$gtable[["grobs"]][[3]]$children[[i]]$gp$fontface == "bold")
{
fig3_legend2$gtable[["grobs"]][[3]]$children[[i]]$gp$fontface <- "italics"
fig3_legend2$gtable[["grobs"]][[3]]$children[[i]]$gp$font <- as.integer(font_for_grob)
}
}
}
## 1346
library("gridExtra")
lay <- rbind(c(1,6),
c(2,7),
c(3,NA),
c(4,NA),
c(5,NA))
fig3_heatmaps <- arrangeGrob(fig3a[[4]],
fig3b[[4]],
fig3c[[4]],
fig3d[[4]],
fig3e[[4]],
fig3_legend1[[4]],
fig3_legend2[[4]],
layout_matrix = lay,
widths=c(4, 0.6),
padding = unit(0.25, "cm"))
ggsave(fig3_heatmaps, filename = "final_figures/Fig3.pdf", width=35, height=30, units='cm', useDingbats = FALSE)
grid.draw(fig3_heatmaps)
fig3f <- readRDS('figures/mcl1_compare/depmap_sanger_20q1.rds')
fig3f$layers[[1]]$aes_params$size <- 1
fig3f <- ggarrange(ggarrange(fig3f + fig1_theme + theme(aspect.ratio=1),
geom_blank(),
ncol = 2,
align="h",
widths=c(1,1),
labels = c("f", "g")),
geom_blank(),
nrow=2,
ncol=1,
heights = c(2, 4))
## Warning in as_grob.default(plot): Cannot convert object of class
## LayerInstanceLayerggprotogg into a grob.
## Warning: Graphs cannot be horizontally aligned unless the axis parameter is set.
## Placing graphs unaligned.
## Warning in as_grob.default(plot): Cannot convert object of class
## LayerInstanceLayerggprotogg into a grob.
ggsave(fig3f, filename = "final_figures/Fig3f.pdf", width=20 , height=26, units='cm', useDingbats = FALSE)
print(fig3f)
fig4a <- readRDS('figures/dependency/ped_v_adult_fraction_dependent_20q1.rds')
fig4b <- readRDS('figures/dependency/ped_v_adult_sig_fraction_dependent_20q1.rds')
fig4c_TRIM8_p <- readRDS('figures/dependency/gene_TRIM8_peds_20q1.rds')
fig4c_TRIM8_a <- readRDS('figures/dependency/gene_TRIM8_adult_20q1.rds')
fig4c_HDAC2_p <- readRDS('figures/dependency/gene_HDAC2_peds_20q1.rds')
fig4c_HDAC2_a <- readRDS('figures/dependency/gene_HDAC2_adult_20q1.rds')
fig4c_IGF1R_p <- readRDS('figures/dependency/gene_IGF1R_peds_20q1.rds')
fig4c_IGF1R_a <- readRDS('figures/dependency/gene_IGF1R_adult_20q1.rds')
fig4c_legend_p <- readRDS('figures/dependency/gene_legend_peds_20q1.rds')
fig4c_legend_a <- readRDS('figures/dependency/gene_legend_adult_20q1.rds')
fig4c_TRIM8_a$labels$y <- "Adult"
fig4c_HDAC2_a$labels$y <- ""
fig4c_IGF1R_a$labels$y <- ""
fig4c_TRIM8_p$labels$y <- "Pediatric"
fig4c_HDAC2_p$labels$y <- ""
fig4c_IGF1R_p$labels$y <- ""
fig4c_TRIM8_a$labels$x <- ""
fig4c_HDAC2_a$labels$x <- ""
fig4c_IGF1R_a$labels$x <- ""
fig4c_TRIM8_p$labels$x <- "TRIM8"
fig4c_HDAC2_p$labels$x <- "HDAC2"
fig4c_IGF1R_p$labels$x <- "IGF1R"
fig4c_theme <- theme(plot.margin = margin(-0.5, -0.5, 0, -0.5, "cm"),
axis.title = element_text(size=8, face="plain"),
plot.background = element_blank())
fig4d <- readRDS('figures/dependency/peds_LRT_deps_GSEA_C5_20q1.rds')
##
## Registered S3 method overwritten by 'enrichplot':
## method from
## fortify.enrichResult DOSE
fig4e <- readRDS('figures/dependency/adult_LRT_deps_GSEA_C5_20q1.rds')
fig4d$labels$title <- "Pediatric selective dependencies"
fig4e$labels$title <- "Adult selective dependencies"
fig4f <- readRDS('figures/dependency/peds_enriched_deps_-0.2_20q1.rds')
fig4 <- ggarrange(ggarrange(fig4a + fig1_theme,
fig4b + fig1_theme +
theme(axis.text.y = element_text(size=4)),
nrow=1,
ncol=2,
align = "h",
labels = c("a", "b")),
ggarrange(
ggarrange(fig4c_TRIM8_p + fig4c_theme ,
fig4c_HDAC2_p + fig4c_theme,
fig4c_IGF1R_p + fig4c_theme,
fig4c_TRIM8_a + fig4c_theme,
fig4c_HDAC2_a + fig4c_theme,
fig4c_IGF1R_a + fig4c_theme,
nrow=2, ncol=3,
align="hv") + theme(plot.margin = margin(0.25, 0.25, 0.25, 1, "cm")),
ggarrange(fig4c_legend_p, geom_blank(), ncol=1, heights=c(1, 2.6)),
ggarrange(fig4c_legend_a, geom_blank(), ncol=1, heights=c(1, 0.05)),
nrow=1,
widths= c(2,1,1),
align="h",
labels = c("c", "", "")),
ggarrange(
ggarrange(fig4d + fig1_theme + theme(axis.text.y = element_text(size=4)) + scale_size(range = c(0,3)),
fig4e + fig1_theme + theme(axis.text.y = element_text(size=4)) + scale_size(range = c(0,3)),
nrow=2, ncol=1, heights = c(1,1), align="v",
labels = c("d", "e")),
fig4f + fig1_theme + theme(legend.position = "bottom"),
ncol = 2,
widths = c(1.5, 1),
labels = c("", "f")),
nrow=3,
ncol=1,
heights = c(2, 1.5, 3))
## Warning in as_grob.default(plot): Cannot convert object of class
## LayerInstanceLayerggprotogg into a grob.
## Warning in as_grob.default(plot): Cannot convert object of class
## LayerInstanceLayerggprotogg into a grob.
## Scale for 'size' is already present. Adding another scale for 'size', which
## will replace the existing scale.
## Scale for 'size' is already present. Adding another scale for 'size', which
## will replace the existing scale.
ggsave(fig4, filename = "final_figures/Fig4.pdf", width=20 , height=26, units='cm', useDingbats = FALSE)
print(fig4)
extfig1_1 <- readRDS('figures/tumor_cl/tumor_cl_reannotated_2_Ewing20q1.rds')
extfig1_2 <- readRDS('figures/tumor_cl/tumor_cl_reannotated_2_Hepatoblastoma20q1.rds')
extfig1_3 <- readRDS('figures/tumor_cl/tumor_cl_reannotated_2_Medulloblastoma20q1.rds')
extfig1_4 <- readRDS('figures/tumor_cl/tumor_cl_reannotated_2_Neuroblastoma20q1.rds')
extfig1_5 <- readRDS('figures/tumor_cl/tumor_cl_reannotated_2_Osteosarcoma20q1.rds')
extfig1_6 <- readRDS('figures/tumor_cl/tumor_cl_reannotated_2_Pediatric CNS PNET20q1.rds')
extfig1_7 <- readRDS('figures/tumor_cl/tumor_cl_reannotated_2_Pediatric Germ Cell20q1.rds')
extfig1_8 <- readRDS('figures/tumor_cl/tumor_cl_reannotated_2_Pediatric Glioma20q1.rds')
extfig1_9 <- readRDS('figures/tumor_cl/tumor_cl_reannotated_2_Pediatric Sarcoma20q1.rds')
extfig1_10 <- readRDS('figures/tumor_cl/tumor_cl_reannotated_2_Renal Medullary Carcinoma20q1.rds')
extfig1_11 <- readRDS('figures/tumor_cl/tumor_cl_reannotated_2_Retinoblastoma20q1.rds')
extfig1_12 <- readRDS('figures/tumor_cl/tumor_cl_reannotated_2_Rhabdoid20q1.rds')
extfig1_13 <- readRDS('figures/tumor_cl/tumor_cl_reannotated_2_Rhabdomyosarcoma20q1.rds')
extfig1_14 <- readRDS('figures/tumor_cl/tumor_cl_reannotated_2_Synovial Sarcoma20q1.rds')
for(plot_to_mod in seq(1, 14, 1))
{
# decrease size of dots
#eval(parse(text=paste0("extfig1_", plot_to_mod, "$layers[[1]]$aes_params$size <- 2")))
# decrease size of text labels and change alpha
eval(parse(text=paste0("extfig1_", plot_to_mod, "$layers[[2]]$aes_params$size <- 1")))
eval(parse(text=paste0("extfig1_", plot_to_mod, "$layers[[2]]$aes_params$alpha <- 1")))
# change label seg line size and alpha
eval(parse(text=paste0("extfig1_", plot_to_mod, "$layers[[2]]$geom_params$segment.size <- 0.2")))
eval(parse(text=paste0("extfig1_", plot_to_mod, "$layers[[2]]$geom_params$segment.alpha <- 0.5")))
# modify legend
eval(parse(text=paste0("extfig1_", plot_to_mod, "$guides$shape$override.aes$size <- 2")))
eval(parse(text=paste0("extfig1_", plot_to_mod, "$guides$fill$override.aes$size <- 2")))
# remove title
eval(parse(text=paste0("extfig1_", plot_to_mod, "$labels$title <- NULL")))
if((plot_to_mod - 1) %% 3 != 0)
{
eval(parse(text=paste0("extfig1_", plot_to_mod, "$labels$y <- NULL")))
}
eval(parse(text=paste0("extfig1_", plot_to_mod, "$labels$x <- NULL")))
}
extfig1_theme <- theme(aspect.ratio=1, legend.text = element_text(size=6), legend.title = element_text(size=6), legend.position = c(1,1))
extfig1 <- ggarrange(extfig1_1 + fig1_theme + extfig1_theme,
extfig1_2 + fig1_theme + extfig1_theme,
extfig1_3 + fig1_theme + extfig1_theme,
extfig1_4 + fig1_theme + extfig1_theme,
extfig1_5 + fig1_theme + extfig1_theme,
extfig1_6 + fig1_theme + extfig1_theme,
extfig1_7 + fig1_theme + extfig1_theme,
extfig1_8 + fig1_theme + extfig1_theme,
extfig1_9 + fig1_theme + extfig1_theme,
extfig1_10 + fig1_theme + extfig1_theme,
extfig1_11 + fig1_theme + extfig1_theme,
extfig1_12 + fig1_theme + extfig1_theme,
extfig1_13 + fig1_theme + extfig1_theme,
extfig1_14 + fig1_theme + extfig1_theme,
nrow=5,
ncol=3,
align="v")
ggsave(extfig1, filename = "final_figures/extFig1.pdf", width=20 , height=26, units='cm', useDingbats = FALSE)
print(extfig1)
extfig2a <- readRDS('figures/tumor_cl/undiff_tumor_cl_rennotated_pediatrics_all20q1.rds')
extfig2b <- readRDS('figures/tumor_cl/tumor_cl_reannotated_uncorrected20q1.rds')
extfig2c <- readRDS('figures/mutation/total_mutations_across_lineages_in_WES_20q1.rds')
extfig2d <- readRDS('figures/mutation/peds_v_other_total_mutations_across_lineages_in_WES_20q1.rds')
extfig2e <- readRDS('figures/mutation/total_COSMICgenefiltered_mutations_across_lineages_in_WES_20q1.rds')
extfig2f <- readRDS('figures/mutation/peds_v_other_COSMICgene_mutations_across_lineages_in_WES_20q1.rds')
# decrease size of dots
extfig2c$layers[[1]]$aes_params$size <- 1
extfig2d$layers[[1]]$aes_params$size <- 1
extfig2e$layers[[1]]$aes_params$size <- 1
extfig2f$layers[[1]]$aes_params$size <- 1
# decrease size of p-value text
extfig2d$layers[[3]]$aes_params$textsize <- 2
extfig2f$layers[[3]]$aes_params$textsize <- 2
# decrease size of dots
#extfig2a$layers[[1]]$aes_params$size <- 1
#extfig2b$layers[[1]]$aes_params$size <- 1
extfig2a$guides$shape$override.aes$size <- 1
extfig2a$guides$fill <- FALSE
extfig2a$labels$title <- "Tumor-cell line expression"
extfig2a$guides$shape$nrow <- 8
extfig2a$guides$shape$title.position <- "top"
extfig2b$guides$shape <- FALSE
extfig2b$guides$fill$override.aes$size <- 1
extfig2b$labels$title <- "Tumor-cell line expression"
extfig2b$guides$fill$title <- "Tumor type"
extfig2b$guides$fill$nrow <- 8
extfig2b$guides$fill$title.position <- "top"
# decrease size of violin outline
extfig2d$layers[[1]]$aes_params$size <- 0
extfig2d$layers[[2]]$geom_params$outlier.alpha <- 0.25
extfig2f$layers[[1]]$aes_params$size <- 0
extfig2f$layers[[2]]$geom_params$outlier.alpha <- 0.25
extfig2 <- ggarrange(
ggarrange(extfig2a + fig1_theme + theme(aspect.ratio=1, legend.position = "bottom"),
extfig2b + fig1_theme + theme(aspect.ratio=1, legend.position = "bottom"),
widths = c(2, 2),
ncol = 2,
nrow = 1,
align = "h",
labels = c("a", "b")),
ggarrange(extfig2c + fig1_theme,
extfig2d + fig1_theme,
widths = c(2, 0.75),
ncol = 2,
nrow = 1,
align = "h",
labels = c("c", "d")),
ggarrange(extfig2e + fig1_theme,
extfig2f + fig1_theme,
widths = c(2, 0.75),
ncol = 2,
nrow = 1,
align = "h",
labels = c("e", "f")),
nrow =3,
ncol=1,
heights = c(3.5, 2.5, 2.5))
ggsave(extfig2, filename = "final_figures/extFig2.pdf", width=20 , height=26, units='cm', useDingbats = FALSE)
print(extfig2)
extfig3a <- readRDS('figures/copy_number/cna_by_lineage_plot20q1.rds')
extfig3b <- readRDS('figures/copy_number/peds_v_adult_cna_plot20q1.rds')
extfig3c <- readRDS('figures/copy_number/pediatric_cn_across_lines20q1.rds')
extfig3d <- readRDS('figures/copy_number/fusions_by_lineage_plot20q1.rds')
extfig3e <- readRDS('figures/copy_number/peds_v_adult_fusion_plot20q1.rds')
# decrease size of dots
extfig3a$layers[[1]]$aes_params$size <- 1
extfig3d$layers[[1]]$aes_params$size <- 1
# decrease size of p-value text
extfig3b$layers[[3]]$aes_params$textsize <- 2
extfig3e$layers[[3]]$aes_params$textsize <- 2
# decrease size of violin outline
extfig3b$layers[[1]]$aes_params$size <- 0
extfig3b$layers[[2]]$geom_params$outlier.alpha <- 0.25
extfig3e$layers[[1]]$aes_params$size <- 0
extfig3e$layers[[2]]$geom_params$outlier.alpha <- 0.25
extfig3 <- ggarrange(
ggarrange(extfig3a + fig1_theme,
extfig3b + fig1_theme,
widths = c(2, 0.75),
ncol = 2,
nrow = 1,
align = "h",
labels = c("a", "b")),
extfig3c,
ggarrange(extfig3d + fig1_theme,
extfig3e + fig1_theme,
widths = c(2, 0.75),
ncol = 2,
nrow = 1,
align = "h",
labels = c("d", "e")),
nrow =3,
ncol=1,
heights = c(3, 3, 3),
labels = c("", "c", ""))
ggsave(extfig3, filename = "final_figures/extFig3.pdf", width=20 , height=26, units='cm', useDingbats = FALSE)
print(extfig3)
extfig4a <- readRDS('figures/mutation/depsLRT_v_total_mutations_across_lineages_in_WES_20q1.rds')
extfig4b <- readRDS('figures/mutation/depsLRT_v_total_COSMICgene_mutations_across_lineages_in_WES_20q1.rds')
extfig4c <- readRDS('figures/copy_number/depsLRT_v_cnas_across_lineages_20q1.rds')
extfig4d <- readRDS('figures/copy_number/depsLRT_v_fusions_across_lineages_20q1.rds')
extfig4e <- readRDS('figures/dependency/total_lrt_dependencies_lineage_size_arranged_20q1.rds')
# decrease size of dots
extfig4a$layers[[1]]$aes_params$size <- 1
extfig4b$layers[[1]]$aes_params$size <- 1
extfig4c$layers[[1]]$aes_params$size <- 1
extfig4d$layers[[1]]$aes_params$size <- 1
extfig4e$layers[[1]]$aes_params$size <- 1
extfig4 <- ggarrange(ggarrange(extfig4a + fig1_theme + theme(aspect.ratio=1),
extfig4b + fig1_theme + theme(aspect.ratio=1),
ncol = 2,
nrow = 1,
align = "h",
labels = c("a", "b")),
ggarrange(extfig4c + fig1_theme + theme(aspect.ratio=1),
extfig4d + fig1_theme + theme(aspect.ratio=1),
ncol = 2,
nrow = 1,
align = "h",
labels = c("c", "d")),
ggarrange(extfig4e + fig1_theme,
geom_blank(),
widths = c(2, 0.75),
ncol = 2,
nrow = 1,
align = "h",
labels = c("e", "")),
nrow =3,
ncol=1,
heights = c(3, 3, 3.5))
## Warning in as_grob.default(plot): Cannot convert object of class
## LayerInstanceLayerggprotogg into a grob.
## Warning: Graphs cannot be horizontally aligned unless the axis parameter is set.
## Placing graphs unaligned.
ggsave(extfig4, filename = "final_figures/extFig4.pdf", width=20 , height=26, units='cm', useDingbats = FALSE)
print(extfig4)
extfig5a <- readRDS('figures/mutation/deps_v_NNMD_20q1.rds')
extfig5b <- readRDS('figures/mutation/deps_v_Cas9_20q1.rds')
extfig5c <- readRDS('figures/mutation/deps_v_growth_20q1.rds')
extfig5d <- readRDS('figures/mutation/deps_v_FDR_TPM0_20q1.rds')
extfig5e <- readRDS('figures/mutation/deps_v_media_20q1.rds')
extfig5f <- readRDS('figures/mutation/deps_v_mets_20q1.rds')
extfig5g <- readRDS('figures/mutation/deps_v_tx_20q1.rds')
# decrease size of dots
extfig5a$layers[[1]]$aes_params$size <- 1
extfig5b$layers[[1]]$aes_params$size <- 1
extfig5c$layers[[1]]$aes_params$size <- 1
extfig5d$layers[[1]]$aes_params$size <- 1
extfig5e$layers[[1]]$aes_params$size <- 1
extfig5f$layers[[1]]$aes_params$size <- 1
extfig5g$layers[[1]]$aes_params$size <- 1
# decrease size of violin outline
extfig5e$layers[[1]]$aes_params$size <- 0
extfig5e$layers[[2]]$geom_params$outlier.alpha <- 0.25
extfig5f$layers[[1]]$aes_params$size <- 0
extfig5f$layers[[2]]$geom_params$outlier.alpha <- 0.25
extfig5g$layers[[1]]$aes_params$size <- 0
extfig5g$layers[[2]]$geom_params$outlier.alpha <- 0.25
extfig5 <- ggarrange(ggarrange(extfig5a + fig1_theme + theme(aspect.ratio=1),
extfig5b + fig1_theme + theme(aspect.ratio=1),
ncol = 2,
nrow = 1,
align = "h",
labels = c("a", "b")),
ggarrange(extfig5c + fig1_theme + theme(aspect.ratio=1),
extfig5d + fig1_theme + theme(aspect.ratio=1),
ncol = 2,
nrow = 1,
align = "h",
labels = c("c", "d")),
ggarrange(extfig5e + fig1_theme + theme(aspect.ratio=1),
extfig5f + fig1_theme + theme(aspect.ratio=1),
extfig5g + fig1_theme + theme(aspect.ratio=1),
ncol = 3,
nrow = 1,
align = "h",
labels = c("e", "f", "g")),
nrow =3,
ncol=1,
heights = c(3, 3, 3))
## Warning in wilcox.test.default(c(35, 23, 19, 19, 28, 30, 18, 29, 23, 28, :
## cannot compute exact p-value with ties
## Warning in wilcox.test.default(c(35, 23, 19, 19, 28, 30, 18, 29, 23, 28, :
## cannot compute exact p-value with ties
## Warning in wilcox.test.default(c(19, 24, 19, 27, 22, 22, 39, 29, 14, 36, :
## cannot compute exact p-value with ties
ggsave(extfig5, filename = "final_figures/extFig5.pdf", width=20 , height=26, units='cm', useDingbats = FALSE)
print(extfig5)
extfig6a <- readRDS('figures/biomarkers/pearson_dist_lrt_genes_20q1.rds')
extfig6b <- readRDS('figures/biomarkers/biomarker_peds_vs_all_20q1.rds')
extfig6 <- ggarrange(extfig6a + fig1_theme,
ggarrange(extfig6b + fig1_theme + theme(aspect.ratio=1),
geom_blank(),
nrow=1,
ncol=2,
widths = c(3,1)),
geom_blank(),
nrow=3,
ncol=1,
heights = c(2, 4, 3),
align = "v",
labels = c("a", "b", ""))
## Warning in as_grob.default(plot): Cannot convert object of class
## LayerInstanceLayerggprotogg into a grob.
## Warning in as_grob.default(plot): Cannot convert object of class
## LayerInstanceLayerggprotogg into a grob.
## Warning: Graphs cannot be vertically aligned unless the axis parameter is set.
## Placing graphs unaligned.
ggsave(extfig6, filename = "final_figures/extFig6.pdf", width=20 , height=26, units='cm', useDingbats = FALSE)
print(extfig6)
extfig7a <- readRDS('figures/dependency/umap_zoom_peds_colored_cell_line_dependency_lrt_20q1.rds')
extfig7b <- readRDS('figures/dependency/expr_clus_v_dep_clus_dep500_expr_2000_20q1.rds')
extfig7c <- readRDS('figures/expression/expr_lineage_similarity_top_2000_20q1.rds')
extfig7d <- readRDS('figures/expression/expression_pcs_dist_to_center_top2000_20q1.rds')
extfig7e <- readRDS('figures/dependency/dep_lineage_similarity_top_500_20q1.rds')
extfig7f <- readRDS('figures/dependency/dep_pcs_dist_to_center_top500_20q1.rds')
# decrease size of dots
extfig7a$layers[[1]]$aes_params$size <- 2
extfig7a$labels$title <- "Dependency embedding"
extfig7a$layers[[2]]$aes_params$size <- 1
extfig7a$layers[[2]]$geom_params$segment.size <- 0.2
extfig7a$layers[[2]]$geom_params$segment.alpha <- 0.5
# decrease size of dots
extfig7b$layers[[1]]$aes_params$size <- 1
extfig7c$layers[[1]]$aes_params$size <- 1
extfig7d$layers[[1]]$aes_params$size <- 1
extfig7e$layers[[1]]$aes_params$size <- 1
extfig7f$layers[[1]]$aes_params$size <- 1
extfig7 <- ggarrange(ggarrange(extfig7a + fig1_theme + theme(aspect.ratio=1, legend.text = element_text(size=6)),
extfig7b + fig1_theme + theme(aspect.ratio=1),
nrow=1,
labels = c("a", "b")),
ggarrange(extfig7c + fig1_theme, extfig7d + fig1_theme, nrow=1, widths = c(2,2),
labels = c("c", "d")),
ggarrange(extfig7e + fig1_theme, extfig7f + fig1_theme, nrow=1, widths = c(2,2),
labels = c("e", "f")),
nrow =3,
ncol=1,
heights = c(4, 2.5, 2.5))
ggsave(extfig7, filename = "final_figures/extFig7.pdf", width=20 , height=26, units='cm', useDingbats = FALSE)
print(extfig7)
extfig8a <- readRDS('figures/mcl1_compare/depmap_demeter_drive_20q1.rds')
extfig8b <- readRDS('figures/biomarkers/pred_features_scatter_MCL1_BCL2L120q1.rds')
extfig8a$layers[[1]]$aes_params$size <- 1
extfig8b$layers[[1]]$aes_params$size <- 1
extfig8 <- ggarrange(ggarrange(extfig8a + fig1_theme + theme(aspect.ratio=1),
extfig8b + fig1_theme + theme(aspect.ratio=1),
ncol = 2,
align="h",
labels = c("a", "b")),
geom_blank(),
geom_blank(),
geom_blank(),
nrow=4,
ncol=1,
heights = c(2, 2, 2, 2),
labels = c("", "c", "d", "e"))
## Warning in as_grob.default(plot): Cannot convert object of class
## LayerInstanceLayerggprotogg into a grob.
## Warning in as_grob.default(plot): Cannot convert object of class
## LayerInstanceLayerggprotogg into a grob.
## Warning in as_grob.default(plot): Cannot convert object of class
## LayerInstanceLayerggprotogg into a grob.
ggsave(extfig8, filename = "final_figures/extFig8.pdf", width=20 , height=26, units='cm', useDingbats = FALSE)
print(extfig8)
extfig9a_ISL1_p <- readRDS('figures/dependency/gene_ISL1_peds_20q1.rds')
extfig9a_ISL1_a <- readRDS('figures/dependency/gene_ISL1_adult_20q1.rds')
extfig9a_HAND2_p <- readRDS('figures/dependency/gene_HAND2_peds_20q1.rds')
extfig9a_HAND2_a <- readRDS('figures/dependency/gene_HAND2_adult_20q1.rds')
extfig9a_GATA3_p <- readRDS('figures/dependency/gene_GATA3_peds_20q1.rds')
extfig9a_GATA3_a <- readRDS('figures/dependency/gene_GATA3_adult_20q1.rds')
extfig9a_legend_p <- readRDS('figures/dependency/gene_legend_peds_20q1.rds')
extfig9a_legend_a <- readRDS('figures/dependency/gene_legend_adult_20q1.rds')
extfig9a_ISL1_a$labels$y <- "Adult"
extfig9a_HAND2_a$labels$y <- ""
extfig9a_GATA3_a$labels$y <- ""
extfig9a_ISL1_p$labels$y <- "Pediatric"
extfig9a_HAND2_p$labels$y <- ""
extfig9a_GATA3_p$labels$y <- ""
extfig9a_ISL1_a$labels$x <- ""
extfig9a_HAND2_a$labels$x <- ""
extfig9a_GATA3_a$labels$x <- ""
extfig9a_ISL1_p$labels$x <- "ISL1"
extfig9a_HAND2_p$labels$x <- "HAND2"
extfig9a_GATA3_p$labels$x <- "GATA3"
extfig9a_PHOX2A_p <- readRDS('figures/dependency/gene_PHOX2A_peds_20q1.rds')
extfig9a_PHOX2A_a <- readRDS('figures/dependency/gene_PHOX2A_adult_20q1.rds')
extfig9a_PHOX2B_p <- readRDS('figures/dependency/gene_PHOX2B_peds_20q1.rds')
extfig9a_PHOX2B_a <- readRDS('figures/dependency/gene_PHOX2B_adult_20q1.rds')
extfig9a_MYOD1_p <- readRDS('figures/dependency/gene_MYOD1_peds_20q1.rds')
extfig9a_MYOD1_a <- readRDS('figures/dependency/gene_MYOD1_adult_20q1.rds')
extfig9a_PHOX2A_a$labels$y <- "Adult"
extfig9a_PHOX2B_a$labels$y <- ""
extfig9a_MYOD1_a$labels$y <- ""
extfig9a_PHOX2A_p$labels$y <- "Pediatric"
extfig9a_PHOX2B_p$labels$y <- ""
extfig9a_MYOD1_p$labels$y <- ""
extfig9a_PHOX2A_a$labels$x <- ""
extfig9a_PHOX2B_a$labels$x <- ""
extfig9a_MYOD1_a$labels$x <- ""
extfig9a_PHOX2A_p$labels$x <- "PHOX2A"
extfig9a_PHOX2B_p$labels$x <- "PHOX2B"
extfig9a_MYOD1_p$labels$x <- "MYOD1"
extfig9a_theme <- theme(plot.margin = margin(-0.5, -0.5, 0, -0.5, "cm"),
axis.title = element_text(size=8, face="plain"),
plot.background = element_blank())
extfig9b <- readRDS('figures/biomarkers/pred_features_HDAC2 (3066)20q1.rds')
extfig9c <- readRDS('figures/biomarkers/pred_features_IGF1R (3480)20q1.rds')
extfig9 <- ggarrange(ggarrange(
ggarrange(extfig9a_ISL1_p + extfig9a_theme ,
extfig9a_HAND2_p + extfig9a_theme,
extfig9a_GATA3_p + extfig9a_theme,
extfig9a_ISL1_a + extfig9a_theme,
extfig9a_HAND2_a + extfig9a_theme,
extfig9a_GATA3_a + extfig9a_theme,
nrow=2, ncol=3,
align="hv") + theme(plot.margin = margin(0.25, 0.25, 0.25, 1, "cm")),
ggarrange(extfig9a_legend_p, geom_blank(), ncol=1, heights=c(1, 1.5)),
ggarrange(extfig9a_legend_a, geom_blank(), ncol=1, heights=c(1, 0.05)),
nrow=1,
widths= c(2,1,1),
align="h"),
ggarrange(
ggarrange(extfig9a_PHOX2A_p + extfig9a_theme ,
extfig9a_PHOX2B_p + extfig9a_theme,
extfig9a_MYOD1_p + extfig9a_theme,
extfig9a_PHOX2A_a + extfig9a_theme,
extfig9a_PHOX2B_a + extfig9a_theme,
extfig9a_MYOD1_a + extfig9a_theme,
nrow=2, ncol=3,
align="hv") + theme(plot.margin = margin(0.25, 0.25, 0.25, 1, "cm")),
geom_blank(),
geom_blank(),
nrow=1,
widths= c(2,1,1)),
ggarrange(extfig9b + fig1_theme + theme(aspect.ratio=1),
extfig9c + fig1_theme + theme(aspect.ratio=1),
ncol = 2,
align="h",
labels = c("b", "c")),
nrow=3,
ncol=1,
heights = c(2, 2, 2),
labels = c("a", "", ""))
## Warning in as_grob.default(plot): Cannot convert object of class
## LayerInstanceLayerggprotogg into a grob.
## Warning in as_grob.default(plot): Cannot convert object of class
## LayerInstanceLayerggprotogg into a grob.
## Warning in as_grob.default(plot): Cannot convert object of class
## LayerInstanceLayerggprotogg into a grob.
## Warning in as_grob.default(plot): Cannot convert object of class
## LayerInstanceLayerggprotogg into a grob.
ggsave(extfig9, filename = "final_figures/extFig9.pdf", width=20 , height=26, units='cm', useDingbats = FALSE)
print(extfig9)
extfig10a <- readRDS('figures/dependency/deps_v_num_CLs_20q1.rds')
extfig10b <- readRDS('figures/dependency/depsLRT_v_num_CLs_20q1.rds')
extfig10c <- readRDS('figures/dependency/tfs_highlighted_enriched_deps_-0.2_20q1.rds')
extfig10a$layers[[1]]$aes_params$size <- 1
extfig10b$layers[[1]]$aes_params$size <- 1
extfig10a$layers[[2]]$aes_params$size <- 1
extfig10b$layers[[2]]$aes_params$size <- 1
extfig10 <- ggarrange(ggarrange(extfig10a + fig1_theme + theme(aspect.ratio=1),
extfig10b + fig1_theme + theme(aspect.ratio=1),
ncol = 2,
align="h",
labels = c("a", "b")),
extfig10c + fig1_theme,
geom_blank(),
nrow=3,
ncol=1,
heights = c(2, 3, 1),
labels = c("", "c"))
## Warning in as_grob.default(plot): Cannot convert object of class
## LayerInstanceLayerggprotogg into a grob.
ggsave(extfig10, filename = "final_figures/extFig10.pdf", width=20 , height=26, units='cm', useDingbats = FALSE)
print(extfig10)
source('setup.R')
## ── Attaching packages ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── tidyverse 1.3.0 ──
## ✓ tibble 3.0.0 ✓ purrr 0.3.3
## ✓ tidyr 1.0.2 ✓ dplyr 0.8.5
## ✓ readr 1.3.1 ✓ forcats 0.5.0
## ── Conflicts ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::combine() masks gridExtra::combine()
## x tidyr::extract() masks magrittr::extract()
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()
## x purrr::set_names() masks magrittr::set_names()
##
## Attaching package: 'matrixStats'
## The following object is masked from 'package:dplyr':
##
## count
## ------------------------------------------------------------------------------
## You have loaded plyr after dplyr - this is likely to cause problems.
## If you need functions from both plyr and dplyr, please load plyr first, then dplyr:
## library(plyr); library(dplyr)
## ------------------------------------------------------------------------------
##
## Attaching package: 'plyr'
## The following object is masked from 'package:matrixStats':
##
## count
## The following objects are masked from 'package:dplyr':
##
## arrange, count, desc, failwith, id, mutate, rename, summarise,
## summarize
## The following object is masked from 'package:purrr':
##
## compact
## The following object is masked from 'package:ggpubr':
##
## mutate
##
## Attaching package: 'plotly'
## The following objects are masked from 'package:plyr':
##
## arrange, mutate, rename, summarise
## The following object is masked from 'package:ggplot2':
##
## last_plot
## The following object is masked from 'package:stats':
##
## filter
## The following object is masked from 'package:graphics':
##
## layout
##
## Attaching package: 'data.table'
## The following objects are masked from 'package:dplyr':
##
## between, first, last
## The following object is masked from 'package:purrr':
##
## transpose
## clusterProfiler v3.14.3 For help: https://guangchuangyu.github.io/software/clusterProfiler
##
## If you use clusterProfiler in published research, please cite:
## Guangchuang Yu, Li-Gen Wang, Yanyan Han, Qing-Yu He. clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS: A Journal of Integrative Biology. 2012, 16(5):284-287.
##
## Attaching package: 'clusterProfiler'
## The following object is masked from 'package:purrr':
##
## simplify
##
## Attaching package: 'scales'
## The following object is masked from 'package:purrr':
##
## discard
## The following object is masked from 'package:readr':
##
## col_factor
##
## Attaching package: 'Seurat'
## The following object is masked from 'package:DT':
##
## JS
## FIt-SNE R wrapper loading.
## FIt-SNE root directory was set to /Users/ndharia/Dropbox (Partners HealthCare)/PedDep Figures/final_code
source('load_data.R')
extended_data_table_2 <- fread('Celligner/PedDep_Celligner_results.csv')
undiff_cluster <- fread('Celligner/undifferentiated_cluster_07022020.csv')
extended_data_table_2 %<>%
dplyr::left_join(., mf %>% dplyr::select(DepMap_ID, CCLE_name, PvA) %>% dplyr::rename(sampleID = CCLE_name), by = "sampleID") %>%
dplyr::mutate(undifferentiated_cluster = sampleID %in% undiff_cluster$sampleID_CCLE_Name) %>%
dplyr::select(sampleID, DepMap_ID, PvA, dplyr::everything()) %>%
dplyr::arrange(type, DepMap_ID)
write.table(extended_data_table_2, file = "figures/tumor_cl/ExtendedDataTable2.txt", sep = "\t", row.names = F)